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c91d7b1 64f5718 c91d7b1 64f5718 c91d7b1 64f5718 c91d7b1 64f5718 c91d7b1 64f5718 c91d7b1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | import torch
import numpy as np
import torch.nn.functional as F
from deeprobust.graph.defense.gcn_guard import GCNGuard
from deeprobust.graph.utils import *
from deeprobust.graph.data import Dataset
from deeprobust.graph.data import PtbDataset, PrePtbDataset
import os
import csv
import argparse
from scipy import sparse
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=15, help='Random seed')
parser.add_argument('--GNNGuard', type=bool, default=True, choices=[True, False])
parser.add_argument('--dataset', type=str, default='Flickr', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
parser.add_argument('--ptb_rate', type=float, default=0.25, help='pertubation rate')
parser.add_argument('--ptb_type', type=str, default='minmax', choices=['clean', 'meta', 'dice', 'minmax', 'pgd', 'random'], help='attack type')
parser.add_argument('--gpu', type=int, default=1, help='GPU device ID (default: 0)')
args = parser.parse_args()
args.cuda = torch.cuda.is_available()
print('cuda: %s' % args.cuda)
device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# data = Dataset(root='/tmp/', name=args.dataset, setting='prognn')
data = Dataset(root='/tmp/', name=args.dataset)
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
ptb_path = f"../attacked_adj/{args.dataset}/{args.ptb_type}_{args.dataset}_{args.ptb_rate}.pt"
perturbed_adj = torch.load(ptb_path)
perturbed_adj = sp.csr_matrix(perturbed_adj.to('cpu').numpy())
def test(adj):
# """defense models"""
''' testing model '''
gcn = GCNGuard(nfeat=features.shape[1], nclass=labels.max().item() + 1, nhid=16, drop=True,
dropout=0.5, with_relu=False, with_bias=True, weight_decay=5e-4, device=device)
gcn = gcn.to(device)
gcn.fit(features, adj, labels, idx_train, train_iters=200, idx_val=idx_val, idx_test=idx_test, verbose=True, attention=args.GNNGuard)
gcn.eval()
# classifier.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
acc_test, _ = gcn.test(idx_test)
# acc_test = classifier.test(idx_test)
return acc_test
def main():
# print('=== testing GCN on original(clean) graph ===')
# test(adj)
#
print('=== testing GCN on Mettacked graph ===')
acc = test(perturbed_adj)
csv_dir = "../result"
os.makedirs(csv_dir, exist_ok=True)
csv_filename = os.path.join(csv_dir, f"GNNGuard_{args.dataset}_{args.ptb_type}_{args.ptb_rate}.csv")
row = [f"{args.dataset} ", f" {args.ptb_type} ", f" {args.ptb_rate} ", f" {acc}"]
try:
file_exists = os.path.isfile(csv_filename)
with open(csv_filename, 'a', newline='') as csvfile:
writer = csv.writer(csvfile)
if not file_exists:
writer.writerow(["dataset ", "ptb_type ", "ptb_rate ", "accuracy"])
writer.writerow(row)
except Exception as e:
print(f"[Error] Failed to write CSV: {e}")
if __name__ == '__main__':
main()
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